nestedtemp {vegan} | R Documentation |
Patches or local communities are regarded as nested if they all could be subsets of the same community. In general, species poor communities should be subsets of species rich communities, and rare species should only occur in species rich communities.
nestedchecker(comm) nestedn0(comm) nesteddisc(comm) nestedtemp(comm, ...) nestednodf(comm, order = TRUE) ## S3 method for class 'nestedtemp': plot(x, kind = c("temperature", "incidendce"), col=rev(heat.colors(100)), names = FALSE, ...)
comm |
Community data. |
x |
Result object for a plot . |
col |
Colour scheme for matrix temperatures. |
kind |
The kind of plot produced. |
names |
Label columns and rows in the plot using names in comm . |
order |
Order rows and columns by frequencies. |
... |
Other arguments to functions. |
The nestedness functions evaluate alternative indices of nestedness.
The functions are intended to be used together with Null model
communities and used as an argument in oecosimu
to analyse
the nonrdanomness of results.
Function netstedchecker
gives the number of checkerboard units,
or 2x2 submatrices where both species occur once but on different
sites (Stone & Roberts 1990).
Function nestedn0
implements
nestedness measure N0 which is the number of absences from the sites
which are richer than the most pauperate site species occurs
(Patterson & Atmar 1986).
Function nesteddisc
implements
discrepancy index which is the number of ones that should be shifted
to fill a row with ones in a table arranged by species frequencies
(Brualdi & Sanderson 1999). The original definition arranges species
(columns) by their frequencies, but did not have any method of
handling tied frequencies.
Function nestedtemp
finds the
matrix temperature which is defined as the sum of “surprises”
in arranged matrix. In arranged unsurprising matrix all species
within proportion given by matrix fill are in the upper left corner of
the matrix, and the surprise of the absence or presences is the
diagonal distance from the fill line (Atmar & Patterson
1993). Function tries to pack species and sites to a low
temperature (Rodríguez-Gironés & Santamaria
2006), but this is an iterative procedure, and the temperatures
usually vary among runs. Function nestedtemp
also has a
plot
method which can display either incidences or temperatures
of the surprises. Matrix temperature was rather vaguely described
(Atmar & Patterson 1993), but
Rodríguez-Gironés & Santamaria (2006) are
more explicit and their description is used here. However, the results
probably differ from other implementations, and users should be
cautious in interpreting the results. The details of calculations
are explained in the vignette
Design decisions
and implementation that you can read using functions
vignette
or vegandocs
.
Function nestednodf
implements a nestedness metric based on
overlap and decreasing fill (Almeida-Neto et al., 2008). Two basic
properties are required for a matrix to have the maximum degree of
nestedness according to this metric: (1) complete overlap of 1's from
right to left columns and from down to up rows, and (2) decreasing
marginal totals between all pairs of columns and all pairs of
rows. The nestedness statistic is evaluated separately for columns
(N columns
) for rows (N rows
) and combined for the whole
matrix (NODF
). If you set order = FALSE
, the statistic
is evaluated with the current matrix ordering allowing tests of other
meaningful hypothesis of matrix structure than ordering by row and
column totals (see Almeida-Neto et al. 2008). Function
nestedness
in the bipartite package is
a direct port of the BINMATNEST programme of
Rodríguez-Gironés & Santamaria (2006).
The result returned by a nestedness function contains an item called
statistic
, but the other components differ among functions. The
functions are constructed so that they can be handled by
oecosimu
.
Jari Oksanen and Gustavo Carvalho (nestednodf
.
Almeida-Neto, M., Gumarães, P., Gumarães, P.R., Loyola, R.D. & Ulrich, W. (2008). A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117, 1227–1239.
Atmar, W. & Patterson, B.D. (1993). The measurement of order and disorder in the distribution of species in fragmented habitat. Oecologia 96, 373–382.
Brualdi, R.A. & Sanderson, J.G. (1999). Nested species subsets, gaps, and discrepancy. Oecologia 119, 256–264.
Patterson, B.D. & Atmar, W. (1986). Nested subsets and the structure of insular mammalian faunas and archipelagos. Biol. J. Linnean Soc. 28, 65–82.
Rodríguez-Gironés, M.A. & Santamaria, L. (2006). A new algorithm to calculate the nestedness temperature of presence-absence matrices. J. Biogeogr. 33, 924–935.
Stone, L. & Roberts, A. (1990). The checkerboard score and species distributions. Oecologia 85, 74–79.
Wright, D.H., Patterson, B.D., Mikkelson, G.M., Cutler, A. & Atmar, W. (1998). A comparative analysis of nested subset patterns of species composition. Oecologia 113, 1–20.
In general, the functions should be used with oecosimu
which generates Null model communities to assess the nonrandomness of
nestedness patterns.
data(sipoo) ## Matrix temperature out <- nestedtemp(sipoo) out plot(out) plot(out, kind="incid") ## Use oecosimu to assess the nonrandomness of checker board units nestedchecker(sipoo) oecosimu(sipoo, nestedchecker, "quasiswap") ## Another Null model and standardized checkerboard score oecosimu(sipoo, nestedchecker, "r00", statistic = "C.score")